Your AI agent demo went perfectly. The team loved it. Leadership is excited. Then you try to put it in production, and everything falls apart.
If this sounds familiar, you’re not alone. We recently surveyed over 150 developers and technical leaders across industries to understand how teams are really building with AI. What we found reveals a critical gap between experimentation and production-ready systems, and it’s costing teams real time and money.
The experimentation trap#
Here’s the state of AI development today: 49% of teams are still experimenting: exploring use cases or prototyping in development. Only 38% have reached what we’d call “mature” adoption, with AI either essential to their business or actively scaling in production.
But here’s what’s more concerning: of those who have pushed agents to production, only 13% feel very confident in their ability to observe and debug AI workflows at scale. Nearly 60% fall somewhere between neutral and not confident at all.
Our findings show that there’s a fundamental mismatch between what today’s AI frameworks enable (fast prototypes) and what enterprises actually need (reliable, observable, production-grade systems).
The hidden tax of fragile AI#
The cost of this gap is something that shows up in the bottom line — we’re far beyond hypothetical scenarios here. 62% of teams report measurable time or revenue losses due to reliability issues. For many, that’s 10–50 developer hours or $1–10k annually. For some, it’s 200+ hours or over $100k per year.
When we asked developers what they’d eliminate with a magic wand, the answers were telling:
- “LLM inconsistencies”
- “Hallucinations. We really depend on accurate responses”
- “Orchestration failures”
48% of respondents identified LLM inconsistency as the most fragile part of their AI systems. And when failures happen, teams lack the observability and recovery mechanisms to handle them gracefully.
What makes enterprise AI different#
Building agents for the enterprise isn’t the same as building a prototype. Enterprise AI systems need to:
- Run reliably over extended periods: Your agent can’t lose state halfway through a multi-day workflow.
- Handle failures gracefully: When an API times out or an LLM hallucinates, the system should retry intelligently, not crash.
- Maintain visibility: You need to know what your agents are doing, why they made specific decisions, and where things went wrong.
- Scale predictably: What works for 10 concurrent agents needs to work for 10,000.
Yet most AI tooling today focuses on iteration speed, not production durability. Agent frameworks give you structure but not orchestration. Memory systems help with context but not state management. Observability tools show you logs but not whether your model outputs are actually correct.
Durable Execution: The missing backbone#
What enterprises need (and what our research shows is most often missing) is orchestration that’s built for durability. The ability for workflows and agents to:
- Automatically track state across long-running processes
- Retry failures without losing progress
- Recover from crashes without developer intervention
- Provide complete visibility into what happened and why
This is what we call Durable Execution, and it’s the backbone today’s agent stacks are missing.
Companies like Replit, ZoomInfo, and Gorgias have already made this shift, moving from brittle, homegrown orchestration to systems designed for production reliability. The results speak for themselves: faster deployment, fewer failures, and teams that can focus on building better agents instead of debugging broken workflows.
№№ Thinking long-term
When we asked respondents about the next 12–24 months, 88% predicted at least a moderate boost to efficiency or revenue from AI, with more than half expecting significant or transformative change.
But growth doesn’t come for free. The top priorities teams identified for the next two years align perfectly with the weaknesses they’re experiencing today:
- Improving reliability and scalability
- Better observability and debugging
- Robust infrastructure that integrates with existing systems
The teams that will win are the ones building systems that last, with the orchestration, observability, and reliability that production AI demands.
Ready to build production-ready AI?#
This post only scratches the surface. Our Production AI stack report dives deeper into:
- Complete breakdown of the modern AI stack (LLMs, agent frameworks, memory, databases, tools, orchestration, observability, and infrastructure)
- Detailed findings from 150+ developers on what’s working and what’s broken
- Comprehensive data visualizations that bring these findings to life
- Real-world case studies from Replit, ZoomInfo, and Gorgias
- Actionable guidance for building agents that survive in production